Kim HC, et al. Clin Hypertens. 2025 Mar;31(1):e11. https://doi.org/10.5646/ch.2025.31.e11

To classify waist circumference (WC) trajectories and examine each trajectory’s association with risk of incident type 2 diabetes mellitus (T2DM). In Korean Genome and Epidemiology Study (KoGES 2001–2014), 4992 participants aged 40 years and above who received biennial health examinations from wave 1 to wave 4 (2001–2008) were selected. Five distinct trajectory groups were identified for WC using group-based trajectory modeling methods such as censored normal model. Cox proportional hazards model was used to examine the association of trajectories with risk of T2DM. During 31,118 person-years of follow-up (mean follow-up duration, 6.2 years), 276 incident cases of T2DM were identified. Through trajectory analysis, 5 distinct WC patterns were found during wave1 to wave 4, which were “Group A” was stable on very low levels, “Group B” was stable on low levels, “Group C” was stable on moderate levels, “Group D” had increasing pattern on elevated levels, “Group E” was shown increasing on high levels. Age-standardized incidences rates per 100,000 person-years were increased with WC expanding trajectory group (193.9 for Group A, 498.4 for Group B, 661.9 for Group C, 1845.9 for Group D, and 2045.0 for Group E). In multivariate analysis after adjusting for confounding variable at wave 4, Group B (Hazard ratio (HR), 2.2; 95% confidence interval (CI), and 1.1–4.6), Group C (HR: 2.5, 95% CI: 1.2–5.0), Group D (HR: 5.4, 95% CI: 2.7–10.9), Group E (HR: 7.3, 95% CI: 3.5–15.4) had a higher risk of T2DM than Group A. After further adjusting for body mass index strongly correlated with WC, the association was attenuated. WC trajectory was a significant predictor of T2DM risk in increasing trajectories on high level. This finding indicate the importance of WC management across prolong lifespan by assessing the prognosis and prevention strategies of high-risk populations for T2DM in middle-aged adults.

This study investigated the association of smoking behaviors, including dual smoking (smoking both cigarettes and e-cigarettes), cigarettes smoking, and previous smoking, with glycated hemoglobin (HbA1c) levels. National Health and Nutrition Examination Survey (KNHANES) data from 2014–2016 was used. Associations between smoking behavior patterns and HbA1c levels were analyzed via multiple regression. Among 8809 participants, individuals who were dual smokers and cigarettes smokers had significantly higher HbA1c levels than non-smokers (dual: β = 0.1116, p = 0.0012, single: β = 0.0752, p = 0.0022). This relationship strengthened in subgroups of men (dual: β = 0.1290, p = 0.0013, single: β = 0.1020, p = 0.0014, ex: β = 0.0654, p = 0.0308), physically inactive subjects (dual: β = 0.1527, p = 0.0053, single: β = 0.0876, p = 0.0197), and overweight (dual: β = 0.1425, p = 0.0133) and obese individuals (dual: β = 0.1694, p = 0.0061, single: β = 0.1035, p = 0.0217). This study suggests that smoking behaviors are likely to increase the risk of HbA1c level in a general population. The health effects of dual smoking remain uncertain and should be addressed in the future.

Choi Y, et al. J Korean Med Sci. 2023 Apr;38(18):e158. https://doi.org/10.3346/jkms.2023.38.e158

Introduction: Smoking among young adults is associated with atherosclerotic cardiovascular disease (ASCVD) in middle age. Our aim was to analyze the trajectory of smoking in young adults and analyze the effects of the trajectory group on incident ASCVD. Methods: This study was conducted among 60,709 young adult men aged 20–29 years who received health screening every two years from 1992–2004. Trajectory analysis was performed through smoking survey data measured 7 times during this period. ASCVD, including ischemic heart disease (IHD) and stroke events were confirmed from 2005–2015. The association between the trajectory group and ASCVD risk was analyzed using Cox proportional hazard models, controlling for covariates and mediators. Results: Trajectory analysis showed that smoking categorized into five groups as follows: Group 1 (28.3%), low steady; Group 2 (14.7%), lowering; Group 3 (17.3%), high steady; Group 4 (15.6%), rise and fall; and Group 5 (24.2%), very high steady. The model performance of the trajectory model (Akaike information criterion; AIC = 51,670.78) with mediators was better than the model (AIC = 51,847.85) without mediators. Group 5 showed a 49% higher risk of ASCVD than Group 1. The risk of IHD was 1.63-times higher for Group 5 and 1.31-times higher for Group 4, compared to Group 1. Compared to Group 1, Group 5 had a 1.36- and 1.58-times higher risk for total stroke and ischemic stroke, respectively. Conclusions: In young adult men, the multiple measured trajectory model with mediators was far more informative than one-time smoking for explaining the association with cardiovascular disease.